How to Cite
Abstract
In practice, however, failures happen without warning and well before scheduled equipment maintenance was due, suggesting a major missed opportunity in implementing more precise and agile maintenance activities. By adopting artificial intelligence and machine learning techniques, researchers are currently pioneering methods that can diagnose faults and predict equipment failures with significantly greater accuracy. In this article, we provide a high-level overview of potential state-of-the-art methodologies for AI-driven predictive maintenance with a focus on AI-driven predictive maintenance methods developed for autonomous vehicle sensor systems. Properly deployed, the methods present an opportunity to enhance the performance of existing maintenance programs while greatly reducing operating costs. Achieving this level of improvement requires the inclusion of large and disparate sets of sensing data, many of which are unstructured or not sampled regularly. By achieving this data representation in the right way, the sensed parameters can become large and rich enough for machine learning models to recognize the spectrum of factors in formulating the likelihood of failures.
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